




'''simple docstring'''



import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_							:	List[Any]	     =		logging.get_logger(__name__)







def       _UpperCamelCase			(_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  str						)-> Any:




     '''simple docstring'''





     __snake_case            =	WavaVecaForSequenceClassification.from_pretrained(_lowerCamelCase ,						config=_lowerCamelCase						)
     __snake_case            =	downstream_dict['''projector.weight''']
     __snake_case            =	downstream_dict['''projector.bias''']
     __snake_case            =	downstream_dict['''model.post_net.linear.weight''']
     __snake_case            =	downstream_dict['''model.post_net.linear.bias''']
     return model







def       _UpperCamelCase			(_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  int						)-> Any:




     '''simple docstring'''





     __snake_case            =	WavaVecaForAudioFrameClassification.from_pretrained(_lowerCamelCase ,						config=_lowerCamelCase						)
     __snake_case            =	downstream_dict['''model.linear.weight''']
     __snake_case            =	downstream_dict['''model.linear.bias''']
     return model







def       _UpperCamelCase			(_lowerCamelCase				:  int ,						_lowerCamelCase				:  Any ,						_lowerCamelCase				:  Union[str, Any]						)-> Tuple:




     '''simple docstring'''





     __snake_case            =	WavaVecaForXVector.from_pretrained(_lowerCamelCase ,						config=_lowerCamelCase						)
     __snake_case            =	downstream_dict['''connector.weight''']
     __snake_case            =	downstream_dict['''connector.bias''']
     for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
          __snake_case            =	downstream_dict[
              f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
          ]
          __snake_case            =	downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

     __snake_case            =	downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
     __snake_case            =	downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
     __snake_case            =	downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
     __snake_case            =	downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
     __snake_case            =	downstream_dict['''objective.W''']
     return model







@torch.no_grad()
def       _UpperCamelCase			(_lowerCamelCase				:  List[str] ,						_lowerCamelCase				:  Any ,						_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  List[Any]						)-> str:




     '''simple docstring'''





     __snake_case            =	torch.load(_lowerCamelCase ,						map_location='''cpu'''						)

     __snake_case            =	checkpoint['''Downstream''']

     __snake_case            =	WavaVecaConfig.from_pretrained(_lowerCamelCase						)
     __snake_case            =	WavaVecaFeatureExtractor.from_pretrained(
         _lowerCamelCase ,						return_attention_mask=_lowerCamelCase ,						do_normalize=_lowerCamelCase						)

     __snake_case            =	hf_config.architectures[0]
     if arch.endswith('''ForSequenceClassification'''						):
          __snake_case            =	convert_classification(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)
     elif arch.endswith('''ForAudioFrameClassification'''						):
          __snake_case            =	convert_diarization(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)
     elif arch.endswith('''ForXVector'''						):
          __snake_case            =	convert_xvector(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)
     else:
          raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}'''						)

     if hf_config.use_weighted_layer_sum:
          __snake_case            =	checkpoint['''Featurizer''']['''weights''']

     hf_feature_extractor.save_pretrained(_lowerCamelCase						)
     hf_model.save_pretrained(_lowerCamelCase						)


if __name__ == "__main__":
     UpperCAmelCase_							:	Any	     =		argparse.ArgumentParser()
     parser.add_argument(
         '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
     )
     parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
     parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
     parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
     UpperCAmelCase_							:	Tuple	     =		parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					['''image_processor''', '''tokenizer''']
 __A			       =					'''ViTImageProcessor'''
 __A			       =					('''CLIPTokenizer''', '''CLIPTokenizerFast''')







 def __init__(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]=None							, lowercase_				:   Dict=None							, **lowercase_				:   List[str])						->	str:


   """simple docstring"""







   _UpperCamelCase           =    None
   if "feature_extractor" in kwargs:
     warnings.warn(
         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
         " instead."							, lowercase_							, )
     _UpperCamelCase           =    kwargs.pop("feature_extractor")

   _UpperCamelCase           =    image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
     raise ValueError("You need to specify an `image_processor`.")
   if tokenizer is None:
     raise ValueError("You need to specify a `tokenizer`.")

   super().__init__(lowercase_							, lowercase_)







 def __call__(		self				:   Any							, lowercase_				:   List[str]=None							, lowercase_				:   int=None							, lowercase_				:   Optional[Any]=None							, lowercase_				:   Dict=None							, **lowercase_				:   Dict)						->	int:


   """simple docstring"""







   if text is None and visual_prompt is None and images is None:
     raise ValueError("You have to specify either text, visual prompt or images.")

   if text is not None and visual_prompt is not None:
     raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt.")

   if text is not None:
     _UpperCamelCase           =    self.tokenizer(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if visual_prompt is not None:
     _UpperCamelCase           =    self.image_processor(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if images is not None:
     _UpperCamelCase           =    self.image_processor(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if visual_prompt is not None and images is not None:
     _UpperCamelCase           =    {
         "pixel_values": image_features.pixel_values,
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
   elif text is not None and images is not None:
     _UpperCamelCase           =    image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   elif visual_prompt is not None:
     _UpperCamelCase           =    {
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
   else:
     return BatchEncoding(data=dict(**lowercase_)							, tensor_type=lowercase_)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, *lowercase_				:   List[Any]							, **lowercase_				:   List[Any])						->	int:


   """simple docstring"""







   return self.tokenizer.batch_decode(*lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, *lowercase_				:   Any							, **lowercase_				:   int)						->	Optional[int]:


   """simple docstring"""







   return self.tokenizer.decode(*lowercase_							, **lowercase_)







 @property
 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	str:


   """simple docstring"""







   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							, lowercase_							, )
   return self.image_processor_class







 @property
 def 		__UpperCAmelCase				(		self				:   int)						->	int:


   """simple docstring"""







   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							, lowercase_							, )
   return self.image_processor




